Five Mistakes of Business Intelligence

By: Jeff Block, Capstone Consulting

Posted: April 10, 2009

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Before you even talk about the technology you'll use to back up your efforts, you must first secure executive commitment (including funding), build consensus among business functions (and conform data dimensions), establish effective data and IT governance, develop a change control management plan to control scope and growth, accurately define the requirements and determine how the system will be supported after it’s built.

There is an order of priority to focus on when launching a BI initiative or project:

The Plan: What exactly are you trying to accomplish, and how does it support the organization’s key performance indicators?

The People: Who is going to accomplish your plan? What roles will they play?

The Process: How will this awesome plan be accomplished?

The Technology: Now that you have all this in place, talk about what tech you’ll use to support the plan, the people and the process.

Bad Idea #2: Acting on a Wrong View of the Data Warehouse

Myths: Everything has to be in the data warehouse. The data warehouse is a copy of my operational data. Data warehouses are fundamentally the same as transactional systems ... And so on.

There are few IT-related concepts more poorly understood than the data warehouse. Dozens, if not hundreds, of disparate definitions and overloaded terms abound. The data warehouse is a highly specialized, highly flexible data architecture that stores the history of dimensionally modeled observations of business processes, optimized for online analytic processing (OLAP).

Data warehouses shouldn’t contain every piece of data you can think of. They aren’t copies of your operational data run on another box for the sake of performance or disparate QOS requirements. They should contain exactly what is needed to represent observations of business processes within your organization in such a way that you can make decisions based on that data. The data thrown off by these processes is collected in the data warehouse in a highly-specialized, optimized way, and then used for reporting actionable decision-making knowledge to end users through a targeted BI presentation layer.

Every piece of data in the warehouse should be for a specific purpose. If you can’t “justify” why a piece of data is in the data warehouse, then it shouldn’t be there. You justify this data by tracing it back to the business case / strategy / top-level key performance indicators of your BI initiative, a particular project, etc. If you can’t do this, then you’re probably dumping data in the warehouse just because you can, not because there’s an actual reason to do so. And that means you run the risk of creating clutter, which will seriously hinder the value of BI and likely cause major regrets down the road.